A Short-Term Load Forecasting Method of Warship Based on PSO-SVM Method

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The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

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569-574

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October 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Hippert HS, Pedreira CE, Souza RC. Neural networks for short-term load forecasting: A review and evaluation. Ieee T Power Syst 2001; 16: 44.

DOI: 10.1109/59.910780

Google Scholar

[2] Cook Chennault KA, Thambi N, Sastry AM. Powering MEMS portable devices - A review of non-regenerative and regenerative power supply systems with special emphasis on piezoelectric energy harvesting systems. Smart Materials and Structures 2008; 17.

DOI: 10.1088/0964-1726/17/4/043001

Google Scholar

[3] Temraz HK, Salama MMA, Chikhani AY. Review of electric load forecasting methods. Canadian Conference on Electrical and Computer Engineering. St. John, Can, 1997. p.289.

DOI: 10.1109/ccece.1997.614846

Google Scholar

[4] Wang L, Li W, Sui T. Review of multiaxial fatigue life prediction technology under complex loading. Advanced Materials Research. Shenyang, China, 2010. p.283.

DOI: 10.4028/www.scientific.net/amr.118-120.283

Google Scholar

[5] Kang C, Xia Q, Zhang B. Review of power system load forecasting and its development. Dianli Xitong Zidonghua/Automation of Electric Power Systems 2004; 28: 1.

Google Scholar

[6] Hagan MT, Behr SM. TIME SERIES APPROACH TO SHORT TERM LOAD FORECASTING. Ieee T Power Syst 1987; PWRS-2: 785.

DOI: 10.1109/tpwrs.1987.4335210

Google Scholar

[7] Vapnik VN. Statistical learning theory. (1998).

Google Scholar

[8] Hong W. Hybrid evolutionary algorithms in a SVR-based electric load forecasting model. Int J Elec Power 2009; 31: 409.

Google Scholar

[9] Hong W. Electric load forecasting by support vector model. Appl Math Model 2009; 33: 2444.

Google Scholar

[10] Wu C, Tzeng G, Lin R. A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Syst Appl 2009; 36: 4725.

DOI: 10.1016/j.eswa.2008.06.046

Google Scholar

[11] Hong W. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energ Convers Manage 2009; 50: 105.

DOI: 10.1016/j.enconman.2008.08.031

Google Scholar

[12] Engelbrecht AP. Fundamentals of computational swarm intelligence: Wiley London, (2005).

Google Scholar